Purpose – Under resting conditions the
cortex exhibits spatio-temporal activity patterns known as resting state
networks (RSNs). These RSNs exhibit occasional massive reorganizations. This
suggests that the cortex is a dynamical system in a critical state (Kitzbichler
et al. 2009). Previous studies have shown that noise, transmission
delays and the underlying structural connectivity are important contributors to
resting state properties. So far, it has not been investigated which graph
theoretical properties of the structural connectivity are the most important
contributors to RSNs. We aim to answer this question employing a
simulation study with network models based on different adjacency matrices.

Method – We performed simulations of
cortical activity during rest with a global spiking attractor model employing
leaky integrate-and-fire neurons and realistic AMPA, NMDA and GABA synapses with
cortical areas interconnected according to a human structural connectivity matrix
and artificially
generated regular, random, small world, and scale adjacency matrices. We
performed both spiking simulations as well as mean field simulations. We
analyzed spiking data using hierarchical clustering to identify functional
networks. Furthermore, we used the clusters to perform lability analyses to
test whether the models resemble systems showing self-organized criticality.
Also, we identified the complete attractor garden; i.e. all functional
networks, of the models in the mean field data.

Results – The spiking model based on human structural
connectivity reproduces empirical functional connectivity well and exhibits
critical dynamics. Those models based on small world and scale free adjacency
matrices resemble the criticality behavior of the human model best. Mean field
analyses show that in the model based on the regular adjacency matrix almost
all areas are active for almost all initial conditions. In the model based on
human structural connectivity only a few areas are active for most initial
conditions while most areas are active in only a handful of conditions. This
can also be observed in models based on the scale free adjacency matrix. The
best predictors for the percentage of initial conditions in which an area
becomes active are the degree to which it is clustered and how extensively it
is connected to other areas.

Conclusions – These preliminary results suggest two
conclusions. Firstly, self-organized criticality observed for the cortex at
rest appears to be mainly related to the cortex’ small world architecture.
Secondly, there exist a number of areas which are both highly clustered and
highly connected in human and scale free matrices that are recruited for almost
all initial conditions. This suggests that a functional network consisting of
these areas; a structural core, can sustain activity most efficiently. The fact
that areas comprising the default mode network (DMN) exhibit exactly these
properties suggests that the DMN is a structural core. A likely function of the
DMN then is to act as a source of activity for other RSNs when external input
is absent. This might explain the inhibition of this network during task
performance because it would lead to interference if the DMN constantly
activates networks not related to the present task.